Determination of Poverty, Unemployment, Economic Growth, and Investment in West Sumatra Province
Bibliographic record
Abstract
This research aims to analyze the relationship between poverty, unemployment, investment, and economic growth in a simultaneous equation system with the factors that influence them. This condition is essential to identify the causes of poverty and unemployment and how investment and economic growth play a role in overcoming these problems. This research uses panel data from 19 districts/cities in West Sumatra from 2015 to 2020. The estimation technique used is a simultaneous equation using several classical assumption tests such as normality, heteroscedasticity multicollinearity, and Granger causality test. The results of this research show that 1) Unemployment, economic growth, education, and health have a significant effect on poverty in West Sumatra, 2) Economic growth, investment, and wages have a significant effect on unemployment in West Sumatra, 3) Unemployment, investment, poverty, and labor have a significant effect on the economic growth in West Sumatra, 4) Economic growth, wages, and taxes have a significant effect on investment in West Sumatra.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".